Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations615
Missing cells31
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.6 KiB
Average record size in memory104.2 B

Variable types

Categorical2
Numeric11

Alerts

ALB is highly overall correlated with PROTHigh correlation
AST is highly overall correlated with GGTHigh correlation
GGT is highly overall correlated with ASTHigh correlation
PROT is highly overall correlated with ALBHigh correlation
Category is highly imbalanced (64.9%) Imbalance
ALP has 18 (2.9%) missing values Missing
CHOL has 10 (1.6%) missing values Missing

Reproduction

Analysis started2025-02-07 15:43:20.738483
Analysis finished2025-02-07 15:43:31.468364
Duration10.73 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Category
Categorical

Imbalance 

Distinct5
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
0=Blood Donor
533 
3=Cirrhosis
 
30
1=Hepatitis
 
24
2=Fibrosis
 
21
0s=suspect Blood Donor
 
7

Length

Max length22
Median length13
Mean length12.82439
Min length10

Characters and Unicode

Total characters7887
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0=Blood Donor
2nd row0=Blood Donor
3rd row0=Blood Donor
4th row0=Blood Donor
5th row0=Blood Donor

Common Values

ValueCountFrequency (%)
0=Blood Donor 533
86.7%
3=Cirrhosis 30
 
4.9%
1=Hepatitis 24
 
3.9%
2=Fibrosis 21
 
3.4%
0s=suspect Blood Donor 7
 
1.1%

Length

2025-02-07T21:43:31.535899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T21:43:31.602635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
donor 540
46.5%
0=blood 533
45.9%
3=cirrhosis 30
 
2.6%
1=hepatitis 24
 
2.1%
2=fibrosis 21
 
1.8%
0s=suspect 7
 
0.6%
blood 7
 
0.6%

Most occurring characters

ValueCountFrequency (%)
o 2211
28.0%
r 621
 
7.9%
= 615
 
7.8%
547
 
6.9%
0 540
 
6.8%
B 540
 
6.8%
d 540
 
6.8%
l 540
 
6.8%
D 540
 
6.8%
n 540
 
6.8%
Other values (16) 653
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7887
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2211
28.0%
r 621
 
7.9%
= 615
 
7.8%
547
 
6.9%
0 540
 
6.8%
B 540
 
6.8%
d 540
 
6.8%
l 540
 
6.8%
D 540
 
6.8%
n 540
 
6.8%
Other values (16) 653
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7887
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2211
28.0%
r 621
 
7.9%
= 615
 
7.8%
547
 
6.9%
0 540
 
6.8%
B 540
 
6.8%
d 540
 
6.8%
l 540
 
6.8%
D 540
 
6.8%
n 540
 
6.8%
Other values (16) 653
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7887
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2211
28.0%
r 621
 
7.9%
= 615
 
7.8%
547
 
6.9%
0 540
 
6.8%
B 540
 
6.8%
d 540
 
6.8%
l 540
 
6.8%
D 540
 
6.8%
n 540
 
6.8%
Other values (16) 653
 
8.3%

Age
Real number (ℝ)

Distinct49
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.40813
Minimum19
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:31.685979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile33
Q139
median47
Q354
95-th percentile64.3
Maximum77
Range58
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.055105
Coefficient of variation (CV)0.21209665
Kurtosis-0.38647336
Mean47.40813
Median Absolute Deviation (MAD)8
Skewness0.26713449
Sum29156
Variance101.10515
MonotonicityNot monotonic
2025-02-07T21:43:31.902502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
46 32
 
5.2%
48 28
 
4.6%
33 25
 
4.1%
51 24
 
3.9%
52 22
 
3.6%
49 21
 
3.4%
50 21
 
3.4%
35 21
 
3.4%
47 20
 
3.3%
43 20
 
3.3%
Other values (39) 381
62.0%
ValueCountFrequency (%)
19 1
 
0.2%
23 1
 
0.2%
25 1
 
0.2%
27 1
 
0.2%
29 2
 
0.3%
30 1
 
0.2%
32 17
2.8%
33 25
4.1%
34 19
3.1%
35 21
3.4%
ValueCountFrequency (%)
77 1
 
0.2%
76 2
 
0.3%
75 1
 
0.2%
74 2
 
0.3%
71 3
 
0.5%
70 3
 
0.5%
68 4
0.7%
67 3
 
0.5%
66 4
0.7%
65 8
1.3%

Sex
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.9 KiB
m
377 
f
238 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters615
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowm
2nd rowm
3rd rowm
4th rowm
5th rowm

Common Values

ValueCountFrequency (%)
m 377
61.3%
f 238
38.7%

Length

2025-02-07T21:43:31.985373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-07T21:43:32.018127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
m 377
61.3%
f 238
38.7%

Most occurring characters

ValueCountFrequency (%)
m 377
61.3%
f 238
38.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
m 377
61.3%
f 238
38.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
m 377
61.3%
f 238
38.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 615
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
m 377
61.3%
f 238
38.7%

ALB
Real number (ℝ)

High correlation 

Distinct189
Distinct (%)30.8%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean41.620195
Minimum14.9
Maximum82.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:32.085816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.9
5-th percentile32.26
Q138.8
median41.95
Q345.2
95-th percentile48.935
Maximum82.2
Range67.3
Interquartile range (IQR)6.4

Descriptive statistics

Standard deviation5.7806294
Coefficient of variation (CV)0.13889001
Kurtosis5.9833006
Mean41.620195
Median Absolute Deviation (MAD)3.15
Skewness-0.17676759
Sum25554.8
Variance33.415676
MonotonicityNot monotonic
2025-02-07T21:43:32.185702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 14
 
2.3%
44.7 12
 
2.0%
39.9 12
 
2.0%
41 12
 
2.0%
46.4 11
 
1.8%
43 10
 
1.6%
41.2 9
 
1.5%
43.4 9
 
1.5%
42 9
 
1.5%
42.9 8
 
1.3%
Other values (179) 508
82.6%
ValueCountFrequency (%)
14.9 1
0.2%
19.3 1
0.2%
20 1
0.2%
20.3 1
0.2%
21.6 1
0.2%
22.5 1
0.2%
23 2
0.3%
24 1
0.2%
24.9 1
0.2%
26.2 1
0.2%
ValueCountFrequency (%)
82.2 1
0.2%
62.9 1
0.2%
59.8 1
0.2%
59.7 1
0.2%
55.4 1
0.2%
54.4 1
0.2%
53.3 1
0.2%
53 1
0.2%
52.4 1
0.2%
52.2 1
0.2%

ALP
Real number (ℝ)

Missing 

Distinct414
Distinct (%)69.3%
Missing18
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean68.28392
Minimum11.3
Maximum416.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:32.285836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.3
5-th percentile36.94
Q152.5
median66.2
Q380.1
95-th percentile104
Maximum416.6
Range405.3
Interquartile range (IQR)27.6

Descriptive statistics

Standard deviation26.028315
Coefficient of variation (CV)0.38117782
Kurtosis54.972905
Mean68.28392
Median Absolute Deviation (MAD)13.7
Skewness4.6549207
Sum40765.5
Variance677.4732
MonotonicityNot monotonic
2025-02-07T21:43:32.385643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.5 5
 
0.8%
61.2 5
 
0.8%
84.1 4
 
0.7%
59.5 4
 
0.7%
63.2 3
 
0.5%
62.9 3
 
0.5%
61.8 3
 
0.5%
60 3
 
0.5%
61.7 3
 
0.5%
69.1 3
 
0.5%
Other values (404) 561
91.2%
(Missing) 18
 
2.9%
ValueCountFrequency (%)
11.3 1
0.2%
19.1 1
0.2%
20.6 1
0.2%
22.9 1
0.2%
26.9 1
0.2%
27 1
0.2%
27.3 1
0.2%
27.5 1
0.2%
28.9 1
0.2%
29.6 2
0.3%
ValueCountFrequency (%)
416.6 1
0.2%
208.2 1
0.2%
190.7 1
0.2%
145 1
0.2%
143.1 1
0.2%
137.8 1
0.2%
137.2 1
0.2%
136.9 1
0.2%
126 1
0.2%
124 1
0.2%

ALT
Real number (ℝ)

Distinct341
Distinct (%)55.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean28.450814
Minimum0.9
Maximum325.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:32.469044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile8.495
Q116.4
median23
Q333.075
95-th percentile62.035
Maximum325.3
Range324.4
Interquartile range (IQR)16.675

Descriptive statistics

Standard deviation25.469689
Coefficient of variation (CV)0.89521827
Kurtosis47.129261
Mean28.450814
Median Absolute Deviation (MAD)7.6
Skewness5.5061135
Sum17468.8
Variance648.70505
MonotonicityNot monotonic
2025-02-07T21:43:32.585689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.6 7
 
1.1%
18.6 6
 
1.0%
19.9 6
 
1.0%
23 5
 
0.8%
17.6 5
 
0.8%
17.2 5
 
0.8%
19.7 5
 
0.8%
25.2 5
 
0.8%
18.3 5
 
0.8%
10.2 5
 
0.8%
Other values (331) 560
91.1%
ValueCountFrequency (%)
0.9 1
0.2%
1.2 1
0.2%
1.3 1
0.2%
2.1 1
0.2%
2.3 1
0.2%
2.4 1
0.2%
2.5 1
0.2%
2.9 1
0.2%
3.5 1
0.2%
3.7 1
0.2%
ValueCountFrequency (%)
325.3 1
0.2%
258 1
0.2%
208.8 1
0.2%
164 1
0.2%
159 1
0.2%
130 1
0.2%
118.1 1
0.2%
118 1
0.2%
114 2
0.3%
103.6 1
0.2%

AST
Real number (ℝ)

High correlation 

Distinct297
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.786341
Minimum10.6
Maximum324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:32.669269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.6
5-th percentile16.67
Q121.6
median25.9
Q332.9
95-th percentile91.84
Maximum324
Range313.4
Interquartile range (IQR)11.3

Descriptive statistics

Standard deviation33.09069
Coefficient of variation (CV)0.95125526
Kurtosis30.836641
Mean34.786341
Median Absolute Deviation (MAD)5.2
Skewness4.940327
Sum21393.6
Variance1094.9938
MonotonicityNot monotonic
2025-02-07T21:43:32.769112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.9 7
 
1.1%
24.3 7
 
1.1%
22 7
 
1.1%
20 6
 
1.0%
17.5 6
 
1.0%
21.2 6
 
1.0%
25.7 6
 
1.0%
19.4 6
 
1.0%
24.7 6
 
1.0%
22.1 6
 
1.0%
Other values (287) 552
89.8%
ValueCountFrequency (%)
10.6 1
0.2%
12 1
0.2%
12.2 1
0.2%
13.1 1
0.2%
13.3 1
0.2%
14.1 2
0.3%
14.7 1
0.2%
14.8 1
0.2%
14.9 1
0.2%
15 2
0.3%
ValueCountFrequency (%)
324 1
0.2%
319.8 1
0.2%
285.8 1
0.2%
263.1 1
0.2%
188.7 1
0.2%
187.9 1
0.2%
187.7 1
0.2%
185 1
0.2%
181.8 1
0.2%
164.2 1
0.2%

BIL
Real number (ℝ)

Distinct188
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.396748
Minimum0.8
Maximum254
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:32.868955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile3
Q15.3
median7.3
Q311.2
95-th percentile24.03
Maximum254
Range253.2
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation19.67315
Coefficient of variation (CV)1.7262073
Kurtosis83.186732
Mean11.396748
Median Absolute Deviation (MAD)2.7
Skewness8.3854367
Sum7009
Variance387.03282
MonotonicityNot monotonic
2025-02-07T21:43:32.952336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 13
 
2.1%
7 12
 
2.0%
6.9 11
 
1.8%
6.1 11
 
1.8%
4.1 11
 
1.8%
5.7 11
 
1.8%
6.3 10
 
1.6%
6.8 10
 
1.6%
3.7 10
 
1.6%
5.8 9
 
1.5%
Other values (178) 507
82.4%
ValueCountFrequency (%)
0.8 1
 
0.2%
1.8 1
 
0.2%
2 1
 
0.2%
2.1 1
 
0.2%
2.2 2
 
0.3%
2.3 2
 
0.3%
2.4 6
1.0%
2.6 3
0.5%
2.7 3
0.5%
2.8 4
0.7%
ValueCountFrequency (%)
254 1
0.2%
209 1
0.2%
200 2
0.3%
119 1
0.2%
117 1
0.2%
91 1
0.2%
67 1
0.2%
59.1 1
0.2%
58 1
0.2%
50 1
0.2%

CHE
Real number (ℝ)

Distinct407
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1966341
Minimum1.42
Maximum16.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:33.035837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.42
5-th percentile4.541
Q16.935
median8.26
Q39.59
95-th percentile11.362
Maximum16.41
Range14.99
Interquartile range (IQR)2.655

Descriptive statistics

Standard deviation2.2056573
Coefficient of variation (CV)0.26909305
Kurtosis1.3147301
Mean8.1966341
Median Absolute Deviation (MAD)1.33
Skewness-0.11023271
Sum5040.93
Variance4.864924
MonotonicityNot monotonic
2025-02-07T21:43:33.285658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.52 6
 
1.0%
9.82 5
 
0.8%
5.95 5
 
0.8%
7.1 5
 
0.8%
8.84 4
 
0.7%
7.93 4
 
0.7%
7 4
 
0.7%
7.5 4
 
0.7%
8.9 4
 
0.7%
7.87 4
 
0.7%
Other values (397) 570
92.7%
ValueCountFrequency (%)
1.42 1
0.2%
1.48 1
0.2%
1.54 1
0.2%
1.57 1
0.2%
1.66 1
0.2%
1.72 1
0.2%
1.73 1
0.2%
1.8 2
0.3%
1.88 1
0.2%
2 1
0.2%
ValueCountFrequency (%)
16.41 1
0.2%
15.43 1
0.2%
15.4 2
0.3%
15.1 1
0.2%
14.8 1
0.2%
13.86 1
0.2%
13.8 2
0.3%
13.71 1
0.2%
13.3 1
0.2%
12.86 1
0.2%

CHOL
Real number (ℝ)

Missing 

Distinct313
Distinct (%)51.7%
Missing10
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean5.3680992
Minimum1.43
Maximum9.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:33.368962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.43
5-th percentile3.622
Q14.61
median5.3
Q36.06
95-th percentile7.29
Maximum9.67
Range8.24
Interquartile range (IQR)1.45

Descriptive statistics

Standard deviation1.1327284
Coefficient of variation (CV)0.21101109
Kurtosis0.69402289
Mean5.3680992
Median Absolute Deviation (MAD)0.73
Skewness0.37582755
Sum3247.7
Variance1.2830737
MonotonicityNot monotonic
2025-02-07T21:43:33.452221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.07 8
 
1.3%
5.1 8
 
1.3%
5.3 7
 
1.1%
5.73 6
 
1.0%
5.9 6
 
1.0%
5.88 5
 
0.8%
4.55 5
 
0.8%
4.68 5
 
0.8%
5.31 5
 
0.8%
5.42 5
 
0.8%
Other values (303) 545
88.6%
(Missing) 10
 
1.6%
ValueCountFrequency (%)
1.43 1
0.2%
2.4 1
0.2%
2.61 1
0.2%
2.79 1
0.2%
2.86 1
0.2%
3.01 1
0.2%
3.02 1
0.2%
3.09 2
0.3%
3.1 1
0.2%
3.19 1
0.2%
ValueCountFrequency (%)
9.67 1
0.2%
9.43 1
0.2%
9.03 1
0.2%
8.89 1
0.2%
8.8 1
0.2%
8.78 1
0.2%
8.6 1
0.2%
8.46 1
0.2%
8.36 1
0.2%
8.28 1
0.2%

CREA
Real number (ℝ)

Distinct117
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.287805
Minimum8
Maximum1079.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:33.552233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile55.55
Q167
median77
Q388
95-th percentile106
Maximum1079.1
Range1071.1
Interquartile range (IQR)21

Descriptive statistics

Standard deviation49.756166
Coefficient of variation (CV)0.61209878
Kurtosis280.10024
Mean81.287805
Median Absolute Deviation (MAD)10
Skewness15.169291
Sum49992
Variance2475.6761
MonotonicityNot monotonic
2025-02-07T21:43:33.635647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 21
 
3.4%
70 20
 
3.3%
72 19
 
3.1%
67 18
 
2.9%
83 17
 
2.8%
64 16
 
2.6%
88 16
 
2.6%
86 15
 
2.4%
69 15
 
2.4%
71 15
 
2.4%
Other values (107) 443
72.0%
ValueCountFrequency (%)
8 1
0.2%
9 1
0.2%
29 1
0.2%
32 1
0.2%
40 1
0.2%
41 1
0.2%
45.4 1
0.2%
48 1
0.2%
49.6 1
0.2%
50 2
0.3%
ValueCountFrequency (%)
1079.1 1
0.2%
519 1
0.2%
485.9 1
0.2%
170 1
0.2%
158.2 1
0.2%
147.3 1
0.2%
136.1 1
0.2%
127 1
0.2%
119 1
0.2%
118.2 1
0.2%

GGT
Real number (ℝ)

High correlation 

Distinct358
Distinct (%)58.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.533171
Minimum4.5
Maximum650.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:33.719268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile10.27
Q115.7
median23.3
Q340.2
95-th percentile108.5
Maximum650.9
Range646.4
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation54.661071
Coefficient of variation (CV)1.3826635
Kurtosis43.712579
Mean39.533171
Median Absolute Deviation (MAD)9.3
Skewness5.6327341
Sum24312.9
Variance2987.8327
MonotonicityNot monotonic
2025-02-07T21:43:33.818458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.5 6
 
1.0%
24.1 6
 
1.0%
19.1 6
 
1.0%
13 6
 
1.0%
15.9 5
 
0.8%
11.4 5
 
0.8%
12.3 5
 
0.8%
20.8 5
 
0.8%
17.4 5
 
0.8%
10.4 5
 
0.8%
Other values (348) 561
91.2%
ValueCountFrequency (%)
4.5 1
 
0.2%
4.9 1
 
0.2%
6.4 1
 
0.2%
7 2
0.3%
7.1 1
 
0.2%
7.2 1
 
0.2%
7.4 1
 
0.2%
7.6 1
 
0.2%
7.9 3
0.5%
8 1
 
0.2%
ValueCountFrequency (%)
650.9 1
0.2%
491 1
0.2%
400.3 1
0.2%
399.5 1
0.2%
392.2 1
0.2%
345.6 1
0.2%
295.6 1
0.2%
273.7 1
0.2%
239 1
0.2%
218.3 1
0.2%

PROT
Real number (ℝ)

High correlation 

Distinct198
Distinct (%)32.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean72.044137
Minimum44.8
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 KiB
2025-02-07T21:43:33.902297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum44.8
5-th percentile64.1
Q169.3
median72.2
Q375.4
95-th percentile80.04
Maximum90
Range45.2
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation5.4026357
Coefficient of variation (CV)0.074990637
Kurtosis3.5445292
Mean72.044137
Median Absolute Deviation (MAD)3
Skewness-0.96368739
Sum44235.1
Variance29.188473
MonotonicityNot monotonic
2025-02-07T21:43:34.001957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.9 15
 
2.4%
73.1 13
 
2.1%
72 9
 
1.5%
69.9 9
 
1.5%
72.4 9
 
1.5%
71.3 8
 
1.3%
70.5 8
 
1.3%
75.2 8
 
1.3%
71.8 8
 
1.3%
71 7
 
1.1%
Other values (188) 520
84.6%
ValueCountFrequency (%)
44.8 1
0.2%
47 1
0.2%
47.8 2
0.3%
51 1
0.2%
53.1 1
0.2%
53.2 1
0.2%
54.2 1
0.2%
56.3 1
0.2%
56.9 2
0.3%
57 1
0.2%
ValueCountFrequency (%)
90 1
0.2%
86.5 1
0.2%
86 1
0.2%
84 1
0.2%
83.4 1
0.2%
83.3 1
0.2%
82.7 1
0.2%
82.6 1
0.2%
82.4 1
0.2%
82.3 1
0.2%

Interactions

2025-02-07T21:43:30.369029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:21.553845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:22.522202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:23.598724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:24.620365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:25.389702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:26.170029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:27.086769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:27.869731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:28.586496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:29.402967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-07T21:43:24.903441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-07T21:43:27.369766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-07T21:43:31.036101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:22.437118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:23.525500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:24.553470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:25.336776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:26.103338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:26.836221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:27.786515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:28.534891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:29.335799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-07T21:43:30.276906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-07T21:43:34.068938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ALBALPALTASTAgeBILCHECHOLCREACategoryGGTPROTSex
ALB1.000-0.0540.1820.027-0.1600.1030.3270.1410.2430.4220.0320.5140.213
ALP-0.0541.0000.2120.0560.179-0.0810.1280.1520.0530.2690.1520.0210.075
ALT0.1820.2121.0000.497-0.0450.1400.3280.1690.2970.3470.4190.2190.200
AST0.0270.0560.4971.0000.0870.3560.102-0.0610.1630.3870.5050.1710.104
Age-0.1600.179-0.0450.0871.000-0.005-0.0530.172-0.0480.2520.100-0.1010.127
BIL0.103-0.0810.1400.356-0.0051.000-0.068-0.1180.1900.2800.2470.1440.019
CHE0.3270.1280.3280.102-0.053-0.0681.0000.4000.2100.3820.1560.2930.187
CHOL0.1410.1520.169-0.0610.172-0.1180.4001.0000.0690.2610.0640.1560.000
CREA0.2430.0530.2970.163-0.0480.1900.2100.0691.0000.2140.1480.1330.000
Category0.4220.2690.3470.3870.2520.2800.3820.2610.2141.0000.3430.4460.077
GGT0.0320.1520.4190.5050.1000.2470.1560.0640.1480.3431.0000.1840.145
PROT0.5140.0210.2190.171-0.1010.1440.2930.1560.1330.4460.1841.0000.046
Sex0.2130.0750.2000.1040.1270.0190.1870.0000.0000.0770.1450.0461.000

Missing values

2025-02-07T21:43:31.152572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-07T21:43:31.236218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-07T21:43:31.402699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CategoryAgeSexALBALPALTASTBILCHECHOLCREAGGTPROT
00=Blood Donor32m38.552.57.722.17.56.933.23106.012.169.0
10=Blood Donor32m38.570.318.024.73.911.174.8074.015.676.5
20=Blood Donor32m46.974.736.252.66.18.845.2086.033.279.3
30=Blood Donor32m43.252.030.622.618.97.334.7480.033.875.7
40=Blood Donor32m39.274.132.624.89.69.154.3276.029.968.7
50=Blood Donor32m41.643.318.519.712.39.926.05111.091.074.0
60=Blood Donor32m46.341.317.517.88.57.014.7970.016.974.5
70=Blood Donor32m42.241.935.831.116.15.824.60109.021.567.1
80=Blood Donor32m50.965.523.221.26.98.694.1083.013.771.3
90=Blood Donor32m42.486.320.320.035.25.464.4581.015.969.9
CategoryAgeSexALBALPALTASTBILCHECHOLCREAGGTPROT
6053=Cirrhosis42f33.079.03.755.7200.01.725.1689.1146.369.9
6063=Cirrhosis49f33.0190.71.236.37.06.923.82485.9112.058.5
6073=Cirrhosis52f39.037.01.330.421.06.333.78158.2142.582.7
6083=Cirrhosis58f34.046.415.0150.08.06.263.9856.049.780.6
6093=Cirrhosis59f39.051.319.6285.840.05.774.51136.1101.170.5
6103=Cirrhosis62f32.0416.65.9110.350.05.576.3055.7650.968.5
6113=Cirrhosis64f24.0102.82.944.420.01.543.0263.035.971.3
6123=Cirrhosis64f29.087.33.599.048.01.663.6366.764.282.0
6133=Cirrhosis46f33.0NaN39.062.020.03.564.2052.050.071.0
6143=Cirrhosis59f36.0NaN100.080.012.09.075.3067.034.068.0